Chat Analytics for Chat Experiences is a robust feature that allows users to review chat performance, and make optimization decisions to help enhance their chat program.
In this article, you'll find information on:
- Chat Data Views
- Chat Analytics Filters & Info Guides
- Chat Data Source & Refresh Processes
- Chat Analytics & Measurement Studio
- Using Chat Analytics to Measure Performance (Use Cases)
Chat Data Views
Chat Analytics provides information about the performance of your Chat programs from several perspectives including:
- Chats: View general Chat performance including volume, lead data, qualification rating, missed chats, other details.
- Bots: Shows Bot performance including volume, playbook effectiveness, response selection popularity, bot time and bot resolutions.
- ABM: See how your target accounts/ABM routing is working to enhance your ABM programs by showing volume, accounts, tags and ABM routing success based on your target account lists in Data Studio. Target accounts used by Terminus chat are configured by administrators via the settings panel under “Terminus Configuration.”
- Reps: See how each of your reps is responding, and how effective they are at engaging your most critical accounts. By hovering over the bars on the graphs, you can see further details such as the name and quantity of chats answered, the average length of time to answer, and the average chat time by rep.
- Routing Groups: See how each of your routing groups are performing. With these reports, you can see the most used routing groups, the average answer rate of a routing group, the most leveraged routing groups and the outcome of those chats that are routed to that group (i.e. were the chats answered, rolled over and how many came in after hours).
- Surveys: Chat users can choose to run a survey at the end of a chat to assess the quality of the visitor experience. This is set through the Data Asks™ experience in the administration panel. In the Surveys view, you can see average survey ratings, the average rating by rep, average rating over your selected timeframe, and average rating by routing group.
- Visitors: In the Visitors view, users can get an understanding of the visitor counts, leads by Data Asks™ types (e.g. email, name, phone), quality, returning visitors and average chat per visitor.
Chat Analytics Filters & Info Guides
Chat Analytics can be filtered by date ranges and by Team.
The reports in each of the Analytics views will have an info icon at the top that will give users additional details on how the report data is derived, and what important actions they might take based on the detail provided.
Chat Data Source & Refresh Processes
Data from Chat Experiences is processed nightly and includes all chats for the previous day. Data will be refreshed in the application around 7AM UTC / midday EST every day.
Because of the real-time nature of chat, Chat Analytics data will not update if an element of the chat changes after the nightly process has completed. For example, if you were to add a tag to a missed chat that occurred yesterday, you will not see that chat reflected in the Tag Volume report, which is available under the "ABM" reporting view.
Chat Analytics & Measurement Studio
All primary reporting for your chat programs will live within the Chat Experiences interface.
In order to see Chat-related performance detail in Terminus Measurement Studio, you will need to first connect your Marketing Automation Platform (MAP) or your Salesforce CRM instance to your Chat Experiences instance. We would advise selecting whichever system you currently manage your lead process in. For example, if you are not currently utilizing the Lead object in Salesforce, we would recommend connecting Terminus Chat to your marketing automation platform.
Having this connection in your Chat Experiences instance will then allow you to compare how your chat programs are performing against your other marketing channels, across our various revenue attribution-focused reports. To learn more about where your chat data will be surfaced in Measurement Studio, see this article.
Using Chat Analytics to Measure Performance
While there are dozens of different options available for reviewing your performance data, here are a few common questions and use cases which might be helpful to focus on while evaluating your chat performance.
Use Case #1: "I need to understand how quickly my reps respond to chats."
Pro-Tip: Chats answered in 5 minutes or less are 900% more likely to result in a conversation as compared to 10+ minutes!
Step 1: In the "Reps" view, use the Answer Timing graph to see how quickly your team is answering chats.
In this example Victoria is averaging a 7.4 second response time and Ben Foley has an 8 second response time. Review your chat response times to see if there are opportunities for improvement or inconsistencies between reps.
Step 2: Look at the quality of chats to confirm you are attracting the right kind of traffic.
The Visitor Quality graph under the "Visitors" view helps you understand how your team is qualifying the chat visitor. In the example above, over 90% of the visitors are qualified, with over 39% of these being considered "very qualified".
Step 3: Are you generating more chats than your team can handle? Should you consider expanding the team, or should you add more qualification bots into your process to prioritize your chats? You can help answer these questions with the Bot Conclusions & Bot Resolution graphs on the "Bot" View.
In this example, we are seeing an almost equal distribution between missed and answered bots, while 93% of our visitors who engage with the bot do not answer enough questions / provide enough information to trigger routing. This is common in websites that proactively launch bots as soon as a visitor arrives. For this use case, we may want to review our bot and make sure our pre-qualification process isn’t proving to be arduous for our visitors.
In reviewing the Missed chats Chart, in late April we saw an increase in the number of missed chats. But, with additional training and support, we have reduced the number of missed chats significantly throughout the month of May, but it looks like we still have some work to do.
Step 4: Finally, review the Routing Groups Outcomes graph in the "Routing Groups" view to see how each routing group is performing.
In this case, we are seeing that almost two-thirds of our inbound team is having to rollover chats during their working hours, (meaning, they are being routed to the team and not answered), while only two of those chats came in after hours. Based on this information, it may be time to investigate why the Inbound team is experiencing so much rollover.
Use Case #2: "I want to know if my target accounts are engaging in chat behavior."
Step 1: In the "ABM" view, see how many conversations you are generating with your target accounts.
In this example, we are generating conversations with our target accounts between 11 and 147 times per day. Looking back at our total chat volume, on our best day in the reporting timeframe (past 30 days), we generated 233 chats in total, and 147 of them were with target accounts.
Step 2: Confirm how much contact data you are collecting on your target accounts.
In the Data Asks/Elements Volume graph (in the "ABM" view), I can see that we have collected 277 names, 281 emails, identified 204 companies, and 197 websites that my sales team can use for outreach, and I can add to nurture campaigns.
Step 3: Create custom & personalized chat experiences for your target accounts.
From the Target Account List chart in the "ABM" view, I can see which accounts have the most chat activity in my reporting timeframe. I can then use insight from the chat history, as well as their engagement signal data in Data Studio, to create personalized chat and ad experiences based on their area of interest, or their intent behaviors.
Step 4: Confirm that your personalization efforts are hitting the market and that you're driving engagement in your most important accounts, using the "Tag Volume" chart in the "ABM" view.
In this example, 122 of our chat conversations with our target account have been tagged by the rep as being “qualified,” with only 1 being tagged as unqualified. This means the traffic I am driving as a marketer and the chat experiences are generating conversations with qualified accounts 99% of the time.
Use Case 3: "I want to ensure that I am driving visitors to web pages that yield the most chat activity."
Step 1: In the "Chats" view, you can review your top performing pages by chat engagement.
In this example, we can see that our most popular page, (not surprisingly) is the homepage. Our next most popular pages are related to our Email Experiences products, and an eBook on account-based marketing strategy. Now that I know where people are engaging the most, I can create compelling chat playbooks that further engage those visitors and drive more conversions.
Step 2: Since we can see that our Email Experiences product & resource pages are driving a lot of chat behavior on our site, we can create account lists in Data Studio to double down through our advertising, web personalization, sales outreach and chat to ensure that we are using the most compelling messaging for our visitors, and creating a "surround sound" approach across multiple channels.
Step 3: Scrolling to the bottom of the "Most Popular Pages for Engagement" chart, you can see the pages with the lowest chat activity. Review your chat bot content, your links to more information, and the total number of visitors to those pages to see if there are opportunities you may not be taking advantage of.
Use Case 4: "Which of my ChatBots are performing best?"
In the "Bots" view, you can review the performance of your bots, compare behaviors, and understand which responses are the most leveraged by your visitors.
Step 1: Use the Bot Volume graph to understand which bots are seeing the most “action” on your site.
In this example, our most popular bots are our Terminus Bot and our Break Bot (Event based campaign-specific bot).
Step 2: Review the Playbook Analysis chart to understand which “plays” are used most often by visitors interacting with a specific Bot.
Here, you can see that the "Question Page" and "Learn More" plays are driving the most engagement for that bot.
Step 3: Using the "Visitor Option Popularity" report, you can go further to understand, of the options on the “Learn More” play, what are my visitors most interested in?
Step 4: Now you can dive deeper into your bots and make any adjustments based on what visitors are finding most compelling, or potentially ask for contact detail as you close the after-hours bot conversation that my internal teams can use to follow up with our visitor in the morning.
These were just a few of the many examples we have to help you drive more Chat programs for your organization. If you have questions related to Chat Analytics, reach out to your Customer Success manager for additional support.